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5215ijpla01

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International Journal of Programming Languages and Applications ( IJPLA ) Vol.5, No.2, April 2015
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ENHANCED SPREADSHEET COMPUTING WITH FINITE-
DOMAIN CONSTRAINT SATISFACTION
Ezana N. Beyenne and Hai-Feng Guo
Department of Computer Science, University of Nebraska at Omaha
Omaha, NE 68182-0500, USA
ABSTRACT
The spreadsheet application is among the most widely used computing tools in the modern society. It
provides great usability and usefulness, and it easily enables a non-programmer to perform programming-
like tasks in a visual tabular “pen and paper” approach. However, due to its mono-directional dataflow,
spreadsheets are mostly limited to bookkeeping-like applications. This paper shows how the spreadsheet
computing paradigm is extended to break through this limitation for solving constraint satisfaction
problems. We present an enhanced spreadsheet system where finite-domain constraint solving is well
supported in a visual environment. A spreadsheet-specific constraint language is constructed for general
users to specify constraints among data cells in a declarative and scalable way. The new spreadsheet
system significantly simplifies the development of many constraint-based applications using a visual
tabular interface. Examples are given to illustrate the usability and usefulness of the extended spreadsheet
paradigm.
KEYWORDS
Spreadsheet computing, Finite-domain constraint satisfaction, Constraint logic programming
1.INTRODUCTION
Due to its great usability and usefulness, the spreadsheet application has been widely used in the
modern computing society. It easily enables a nonprogrammer perform programming-like tasks,
such as personnel information management, computerized bookkeeping, and electronic financial
planning, in a tabular “pen and paper” approach. Each cell in a spreadsheet may contain a value
in some data type or a formula which depends on other cells; then when the other cells are
updated, the dependent cell is automatically updated as well. Such a mono-directional dataflow
makes the spreadsheets fairly easy for general users to understand and manipulate. However, it
also hampers the further uses of spreadsheets on many daily-life constraint problems, such as
resource allocation, task scheduling, and timetabling problems.
A few research efforts have been conducted to extend spreadsheets with more powerful
computational tools for solving domain-specific problems. For instance, the spreadsheet interface
for sales/products configuration system [7] was developed by incorporating a light-weight
constraint satisfaction solver (ILOG Solver) as an MS Excel add-on. PrediCalc [12] is a
spreadsheet system which allows general logical constraints for data management systems;
logical constraints, constructed using cell names and the usual logical connectives and the
quantifiers, are used to maintain the consistency among databases. IntervalSolver [10] is a
spreadsheet extension that allows users to take uncertainties of values into account by using
intervals; it is capable of dealing with rounding errors, imprecise data and numerical constraints
on intervals.

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International Journal of Programming Languages and Applications ( IJPLA ) Vol.5, No.2, April 2015
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More general extensions combine a spreadsheet paradigm with other programming paradigms,
such as constraint logic programming. PERPLEX [2] is a programming environment combining
the power of logic programming with the spreadsheet interface; it allows users to define
predicates among data cells in an interactive way. NExSched [6] is a spreadsheet interface for
solving constraint satisfaction problems following the knowledge-sheet paradigm [9]; it
incorporates a finite-domain constraint solver (SICStus) as a plug-in for Microsoft Excel.
NEXCEL [4, 5, 15] proposed a deductive extension of the spreadsheet paradigm for reasoning
and decision-making applications; it allows users to define logical inference rules for symbolic
reasoning, and values satisfying them are visualized on the fly and propagated to any cells which
depend on them.
All of these extensions essentially provide a spreadsheet interface for constraint (logic)
programming or solving domain-specific problems, which require that the defined predicates,
constraints, and inference rules follow the correct syntax of underlying programming paradigm.
In other words, it requires users to have certain programming skills or experiences on solving
domain-specific problems. Those extensions certainly improve the usefulness of spreadsheets;
however, without improving the usability at the same time, all extensions are nearly inaccessible
to mainstream spreadsheet users. From this point of view, unless the extensions attract
mainstream spreadsheet users, the practical contribution of these extensions is limited since
domain experts would rather use an underlying programming paradigm directly. For this reason,
it is very important to have a user-centric approach to functions in Excel. As emphasized in [11]
by Jones, et al., “the commercial success of spreadsheets is largely due to the fact that many
people find them more usable than programming languages for programming-like tasks.”
In this paper, we present a development framework that incorporates spreadsheets with a finite-
domain constraint programming paradigm, where each spreadsheet cell can be attached to a finite
domain or a constraint specifying the relation among cells. For an experienced programmer,
scalability issues are usually handled nicely by either recursion or loop statements. However, for
a general spreadsheet user, a larger-size problem usually requires large number of constraints to
be specified without the assistance of recursion or loops. This can make the spreadsheet interface
impractical and quite error-prone for constraint programming. Therefore, we provide a set of new
functions for spreadsheet users to specify the constraints among data cells in a declarative and
scalable way. The new framework significantly simplifies the development of many constraint-
based applications using a visual spreadsheet interface.
We developed an extension tool, named CSeE (Constraint Satisfaction extended Excel), between
Microsoft Excel and SWI Prolog. This work focuses on merging the CLP(FD)1
paradigm with the
Excel’s spreadsheet paradigm in such a way that it hides the complexities of CLP(FD) by
providing an add-in to Excel in the form of menus and function extensions. These menus and
functions will enable the end users to specify constraints that will interact with a back-end
constraint solver (SWI Prolog). The general user will be able to use the extended spreadsheet to
explore new ways of solving many problems (e.g., scheduling problems, puzzle solving, etc.) that
have previously proved to be an arduous, time-consuming task and also were inaccessible due to
syntactic and semantic complexities. The extended spreadsheet will become an even more
indispensable tool in solving previously complex and error-prone problems that they encounter in
their daily lives. The CSeE tool also provides an interface for an experienced constraint
programmer to define new predicates and propagation rules. .

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International Journal of Programming Languages and Applications ( IJPLA ) Vol.5, No.2, April 2015
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Recently, a new Excel solver [16, 17] has been proposed as an education platform for
management science. The solver delivers great usability and quality on solving optimization,
simulation, and decision science problems. Different from the Excel solver, our CSeE system
applies an AI-based reasoning approach and multi-directional dataflow models [1, 13] to solve
general finite-domain constraint satisfaction problems. Our system further provides a set of
spreadsheet-specific constraints in the form of Excel functions to handle large problems in a
declarative and scalable way.
The remaining parts of this paper are organized in the following way: Section 2 gives a brief
introduction of constraint programming. Section 3 shows the architectural design of the CSeE
system, which enhances the Microsoft Excel system with a spreadsheet-specific constraint
language (SSCL) and constraint satisfaction capability. Section 4 introduces the syntax and
informal semantics of the SSCL constraints. Section 5 explains how the constraints defined in the
spreadsheet layer are integrated to an executable constraint logic program. Three constraint-based
spreadsheet applications are given in Section 6 to illustrate the usability and usefulness of the
CSeE tool. Finally, the conclusion and future extensions are addressed in Section 7.
2. CONSTRAINT PROGRAMMING
Constraint programming (CP) [1, 13] is a computational programming paradigm for formulating
and solving problems based on constraints. A constraint is a description of the logical relation
among several variables, where each variable takes values from a given domain. A constraint is
therefore used to determine the values that variables can take, representing some partial
information about related variables.
Constraints are a natural medium to express all sorts of daily reasoning problems. For example,
the following constraints can be used to describe a relation among the three angles (A1, A2 and the
right angle A3) in a right triangle:
A1 + A2 + A3 = 180 and A3 = 90,
and a simple loan can be represented as
B = P + I × P,
where B, I, and P are the final balance, the interest rate, and the principal of the loan, respectively.
CP is an emergent software technology for specifying and solving many types of combinatorial
and optimization problems in a declarative yet effective way. The idea of constraint programming
is to model and solve problems by specifying constraints which must be satisfied in the problems.
An important characteristic of constraint programming is its declarative nature. A CP
programmer does not need to specify the computational procedure that should be used to enforce
the constraints; rather, they just state what constraints must hold and leave them up to a
computational system to solve.
In this paper, we focus on an important domain of constraints, called finite-domain constraints,
since probably more than 95% of the current industrial constraint applications use finite domains
[1]. Constraint Satisfaction is an algorithmic study of solving finite-domain constraints by the
Artificial Intelligence community. A Constraint Satisfaction Problem (CSP) can be formally
defined as
• Variables: a set of variables X = { x1, · · · , xn },

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International Journal of Programming Languages and Applications ( IJPLA ) Vol.5, No.2, April 2015
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• Domains: for each variable xi, a finite set Di of possible values, and
• Constraints: a set of constraints restricting the values that the variables can take
simultaneously.
Constraint satisfaction is to find solutions of a CSP by assigning each variable a value from its
domain, which satisfy all the constraints. Often there are multiple solutions that satisfy the
constraints. In an optimization problem, one can specify a criterion to choose the optimal solution
among the solution choices.
Example 1. The Knapsack Problem: A smuggler has a knapsack that has limited capacity of
only 9 units. He can smuggle bottles of whiskey of consisting of 4 units, perfume bottles of 3
units and 2 unit cartons of cigarettes. The profit from smuggling a bottle of whiskey is $15, while
a bottle of perfume is $10 or a carton of cigarettes is $7. The smuggler can only make the trip if
he can make a profit of greater than or equal to $30. What are the combinations that will
maximize his profit?
A constraint program, solving the Knapsack problem, is shown as follows:
domain([W,P,C], 0, 9), (1)
4*W + 3*P + 2*C <= 9, (2)
15*W + 10*P + 7*C >= 30, (3)
maximize(labeling([W,P,C]), 15*W + 10*P + 7*C). (4)
The structure of the program follows the definition of a constraint satisfaction problem. Line (1)
introduces a set of variables {W, P, C}, denoting the quantities of whiskey bottles, perfume
bottles, and cigarette cartons to smuggle and also defines their domains, an integral range from 0
to 9. The rest of code specifies the constraints declared in the problem, where the last line is an
optimization predicate to search the value space of {W, P, C} to maximize the profit objective, 15
∗ W + 10 ∗ P + 7 ∗ C. The built-in predicate labeling(L) assigns concrete possible values to the
variables in L from their given domains.
To find more detailed introduction and algorithms on constraint satisfaction, please refer to [1,
13].
3. THE CSEE SYSTEM — DESIGN ARCHITECTURE
The CSeE system enhances the Microsoft Excel with a spreadsheet-specific constraint language
(SSCL) and constraint satisfaction capability. The enhancement is currently implemented as a
COM Add-In for MS Excel 2003 developed using the C# and Visual Studio Tools for Office
(VSTO) 2005. The spreadsheet-specific constraint language (SSCL) is a set of easy-to-use Excel
functions, which can easily be scaled to specify large problems in a consistent form. We use the
SWI-Prolog as the back-end constraint solver. The bi-directional communication between the
Excel and SWI-Prolog is achieved by language processing and transformation techniques.
The overall architecture of our CSeE system is shown in Figure 1. The user-centric design of the
architecture focuses on both the usability and usefulness of the system. The new Excel
spreadsheet provides menus, submenus, and access to spreadsheet-specific constraint language
(SSCL), in a form of Excel functions, to allow users to model and program constraint satisfaction
problems in a scalable yet similar visual manner of using Microsoft Excel. The language
processing layer parses the spreadsheet cell inputs, compiles to the objective CLP code, and
integrates the compiled code with a pre-defined CSeE library for SWI-Prolog to generate an

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International Journal of Programming Languages and Applications ( IJPLA ) Vol.5, No.2, April 2015
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executable CLP program. Additionally, the language processing layer decomposes the query
result, which is returned from the constraint solver, into cell values, so that the spreadsheet will
be properly updated with solutions.
Figure 1: The CSeE System Architecture
The constraint solver layer is comprised of a .NET wrapper, named CSeEWrapper, which acts as
the communication channel between the SWI Prolog and other applications in the .NET
framework. The CSeEWrapper is a class that wraps the SWI Prolog’s plcon.exe window using
the .NET 2.0’s System.Diagnostics.Process namespace, thereby enabling the CSeE System to
interface with SWI-Prolog. Rich class libraries, templates, languages and technologies make the
.NET framework an ideal platform for developing both the communication interface and Office
applications. In addition to reusability, the framework allows developers to use a multitude of
languages, such as C# or VB.NET, to manipulate the Office application’s object model and
interface with third party software in a seamless managed environment. One such library
provided by the .NET Framework’s is the System Diagnostics library, which is used to wrap
SWI-Prolog’s command window. The library provides functionality to send data (e.g., programs
generated from the parser, queries) and receive data (e.g., results from the parser) from SWI-
Prolog’s command window, in a synchronous mode. It also allows the SWI-Prolog functionality
to be ported to other .NET applications.
4. SPREADSHEET-SPECIFIC CONSTRAINT LANGUAGE (SSCL)
Spreadsheets are a popular paradigm due to their ease of use and usefulness that has enabled non-
programmers to perform programming like tasks in a familiar tabular “pen and paper” setting. For
the same reason, we present a spreadsheet-specific constraint language (SSCL) for general users
to specify constraint satisfaction problems over the Excel spreadsheets. The SSCL language
provides familiar and concrete representations of domain variables and their values, as well as
constraints on these domains in the form of functions. Typically, constraint logic programming
relies on control statements, such as recursion, to handle large-scale problems. Without the aid of
recursion, spreadsheet users will find that as the scale of the problem increases, a larger number
of constraints must be specified to solve constraint satisfaction problems. The SSCL adds the
benefit of hiding the syntactic and semantic complexity of constraint logic programming, while at

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the same time providing flexibility to specify large-scale applications. The SSCL introduces a set
of constraint functions that will allow users to specify constraints that exist among cells in a
declarative and scalable way.
The design of SSCL follows the basic principle of defining a constraint satisfaction problem,
which includes a set of finite-domain variables and a finite set of constraints defined over them.
Besides standard use of the spreadsheet cell, each cell can further be attached to
• a finite domain; a spreadsheet cell, which is attached to a finite domain, is often called a
cell variable;
• a constraint describing the relationship that exists among other cell variables;
• an optimization constraint; or
• a variable/constraint range specification.
We describe the syntax and informal meaning of the SSCL constraints in the follow subsections.
Each SSCL constraint has a prefix of ss, and its syntactic conventions in regard to the MS Excel
cell range is consistent to Excel’s A1 reference style [3], which refers to column headings starting
with letters beginning from A through IV ( a total of 256 columns) and refers to row headings
consisting of numbers from 1 through 55,536. For example, A1 refers to the cell in column A and
row 1; and B5:E10 refers to the range of cells in columns B through E and rows 5 through 10.
4.1. Domain Specification
The finite domain can be specified individually on a cell (e.g., A1) as follows:
• an integer list (or an integer): The cell variable A1 can take a value in an integer list
within a pair of square brackets (e.g., a list [2, 5, 7]).
• Lower..Upper: The cell variable A1 can take an integer value between Lower and Upper;
for example, 2..5 means an integer list [2, 3, 4, 5].
The finite domain can also be specified for a cell range using a domain constraint, in a form of
ssDomain(CellRange, Min, Max) or ssDomain(CellRange, IntegerList), where each cell variable
in CellRange can take a value from the specified finite domain. For example, ssDomain(A1:H8,
0, 1) defines Boolean variables among cells A1 through H8.
4.2. Constraint Specification
A constraint describes a relation among cell variables that a solution must satisfy. Let A1 and B2
be two cell variables whose domains are set to 1..10. Thus, A1+4 < B2 is a finite-domain
arithmetic constraint. Using consistency techniques [1, 13] in the constraint solver SWI Prolog,
the CSeE system is able to automatically reduce the domains of A1 and B2 to 1..5 and 6..10,
respectively. The consistency techniques can discover and remove all of the invalid values. For
example, 6 is invalid in A1’s domain because there is no value from the domain of B2 to support
the constraint 6 + 4 < B2. Therefore, 6 is removed from A1’s domain automatically.
4.2.1. Spreadsheet-Specific Constraints
Besides the regular relational constraints and symbolic arithmetic expressions supported directly
by SWI-Prolog [14], the SSCL language provides a set of spreadsheet-specific constraints defined
as follows:

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• ssNthElement(N, L, V): V is constrained to be equal to the N-th element of the list L,
where V is a cell variable, and L is a list of integers or a cell variable range.
• ssAllDifferent(CTL:CBR): All cell variables in the cell range from the top left cell, CTL, to
the bottom right cell, CBR, are pairwise non-equal.
• ssRowsAllDifferent(CTL:CBR): The cell variables in each same row, within the cell range
CTL:CBR, are pairwise non-equal. For example, ssRowsAllDifferent(A1:C2) is equivalent to
•
A1 ≠ B1, A1 ≠ C1, B1 ≠ C1,
A2 ≠ B2, A2 ≠ C2, B2 ≠ C2.
• ssColsAllDifferent(CTL:CBR): The cell variables in each same column, within the cell
range CTL:CBR, are pairwise non-equal.
• ssDiagonalsAllDifferent(CTL:CBR): The cell variables in each diagonal cells, within the
cell range CTL:CBR, are pairwise non-equal. For example, the following constraint
ssDiagonalsAllDifferent(A1:D3) is equivalent to
A2 ≠ B3,
A1 ≠ B2, A1 ≠ C3, B2 ≠ C3,
B1 ≠ C2, B1 ≠ D3, C2 ≠ D3,
C1 ≠ D2.
• ssBackDiagonalsAllDifferent(CTL:CBR): The cell variables in each same back-diagonal
cells, within the cell range CTL:CBR, are pairwise non-equal.
• ssRowsAggregate(ArithOp, CTL:CBR, RelOp, IntList): The values of all cell variables
in i-th same row, within the range of CTL:CBR, are aggregated together using the binary arithmetic
operator ArithOp, and the result has the binary relation RelOp with the i-th integer in the list
IntList. For example, ssRowsAggregate(+, A1:C2, <, D1:D2) means that
A1 + B1 + C1 < D1, A2 + B2 + C2 < D2.
If all of the integers in the IntList are same, they can be replaced with a single integer. For
example, ssRowsAggregate(+, A1:C2, <=, 10) means that
A1 + B1 + C1 ≤ 10, A2 + B2 + C2 ≤ 10.
• ssColsAggregate(ArithOp, CTL:CBR, RelOp, IntList): The values of all cell variables in
i-th same column, within the range of CTL:CBR, are aggregated together using the binary arithmetic
operator ArithOp, and the result has the binary relation RelOp with the i-th integer in the list
IntList.
• ssDiagonalAggregate(ArithOp, CTL:CBR, RelOp, IntList): Similar to the previous
constraint, except that ssDiagonalAggregate/4 applies aggregation on each same diagonal cells.
For example, ssDiagonalAggregate(+, A1 : C2, >, [2,5,4,6]) is equivalent to
A2 > 2, A1 + B2 > 5, B1 + C2 > 4, C1 > 6.
• ssBackDiagonalAggregate(ArithOp, CTL:CBR, RelOp, IntList): Similar to the previous
constraint, except that ssBackDiagonalAggregate/4 applies aggregation on each same back-
diagonal cells.
• ssPairsOp(C1TL:C1BR, ArithOp, C2TL:C2BR, RelOp, IntList): This constraint is used to
apply the binary operator ArithOp on each pairwise i-th operands from the cell ranges C1TL:C1BR

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and C2TL:C2BR, and the result has the binary relation RelOp with the i-th integer in the list IntList.
For example, ssPairsOp(A1:B2, *, C1:D2, =, E1:F2) equals to
A1 * C1 = E1, B1 * D1 = F1, A2 * C2 = E2, B2 * D2 = F2.
4.2.2. Optimization Constraints
• ssMin(V): The minimal optimization constraint is used to find a solution to the constraint
problem, where the value of the cell variable V is minimized.
• ssMax(V): The maximal optimization constraint is used to find a solution to the
constraint problem, where the value of the cell variable V is minimized.
4.2.3. Variable/Constraint Range Specification
Users are required to specify the ranges of the cell variables and constraints, so that the CSeE
system could easily identify all the finite-domain cell variables and their related constraints. The
absence of these specifications will require the parsing of the entire spreadsheet or special
indication of each constraint (e.g., within a pair of squared brackets); however, both alternatives
could cause unnecessary ambiguous issues. Hence, we introduce two extra constraints as follows:
• ssVarRange(CellRange): The cell variables are located in CellRange, where multiple
ranges can be put together; e.g., ssVarRange([A1:C3,E6:F9,G10]).
• ssConstraintRange(CellRange): The constraints are located in CellRange.
5. Language Processing Layer
The spreadsheet language processing layer gathers all the necessary input information (namely,
cell variables, domains and constraints), parses through the Excel’s A1 reference style [3],
transforms the spreadsheet-specific constraints into SWI-Prolog’s syntax, and integrates with the
pre-defined CSeE library for SWI-Prolog into an executable CLP program. The language
processing layer is also responsible for extracting and formatting values for each cell variable
once the answers are returned from the constraints solver. In this section, we mainly address the
transformation rules, the CSeE library, and program integration in details.
5.1. Cell Range Transformation
We first introduce transformation rules to convert a cell range in Excel’s styles into a list of
variables. As shown in Figure 2, given an input of a cell range C, the function varList(C) creates a
list L of cell variables, and the function varListByRow(C), where C is in the form of CTL : CBR,
creates a list L containing sub-lists of cell variables grouped by rows. For example,
varListByRow(A1:B3) returns [[A1,B1],[A2,B2],[A3,B3]]. Similarly, the following
transformation functions are defined as well:
• varListByCol(CTL : CBR) creates a list containing sub-lists of cell variables grouped by
columns.
• varListByDiag(CTL : CBR) creates a list containing sub-lists of cell variables grouped by
diagonals. For example, the constraint varListByDiag(A1:B3) returns a nested list
[[A3],[A2,B3],[A1,B2],[B1]].

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• varListByBackDiag(CTL : CBR) creates a list containing sub-lists of cell variables
grouped by back diagonals. For example, the constraint varListByBackDia(A1:B3) returns a list
[[A1],[A2,B1],[A3,B2],[B3]].
Figure 2: Algorithms of varList and varListByRow
5.2. Constraint Transformation
In this subsection, we describe the major transformation rules, itemized as follows, from the
spreadsheet cell inputs to the SWI-equivalent constraints. The transformation function is
described in a form of
where Intermediate Steps are the auxiliary functions to make the transformation happen. We
focus on those transformation rules which are particularly related to spreadsheets, and the rules
for those constraint inputs which are not listed below are defined in a similar way.
• domain2swi converts a domain specification ssDomain to a SWI constraint using
the following two rules, where ← means assignment. The finite domain (e.g., a single
integer V, or a range Min..Max) can also be specified individually on a cell C. In such
cases, constraints (e.g., C = V or C in Min..Max) for each cell C will be created.
• allRowDiff2swi converts the spreadsheet constraint ssRowsAllDifferent to an
SWI equivalent as follows, where the predicate subListAllDifferent/1, pre-defined in the
CSeE library for SWI-Prolog, means that the values for the cell variables in each sublist
are all different.
• allDiaDiff2swi converts the constraint ssDiagonalsAllDifferent in a similar way,
except calling the rule varListByDiagonal. So are allColDiff2swi and allBDiaDiff2swi

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for converting constraints ssColsAllDifferent and ssBackDiagonalsAllDifferent,
respectively, to the same SWI predicate subListAllDifferent/1.
• rowsAgg2swi is defined as follows, where subListAggregate/4 is defined in the
CSeE library to aggregate the items among each sublist using given binary operators Bo
and Ro.
• diaAgg2swi is defined in a similar way for the constraint on diagonal
aggregation. So are colsAgg2swi and bdiaAgg2swi for converting the constraints on
column aggregation and back diagonals, respectively, to the same subListAggregate/4.
• pairsOp2swi is for ssPairsOp, where the SWI predicate pairAggregate/5 is
defined in the CSeE library to apply the arithmetic operator Bo on each pairwise items in
lists L1 and L2, and then connects to the corresponding item in the list L3 by applying the
relation operator Ro.
5.3. The CSeE Library for SWI-Prolog
The CSeE system provides a module file, named “csee.pl”, for SWIProlog. The module file
contains a set of built-in predicates, such as subListAllDifferent/1 and subListAggregate/4 shown
below, which will be loaded automatically when the Prolog engine is started:
:- module(csee, [ subListAllDifferent/1,
subListAggregate/4,
pairsAggregate/5, ... ]).
subListAllDifferent([ ]).
subListAllDifferent([F | R]) :-
all_different(F),
subListAllDifferent(R).
pairsAggregate([ ], _, [ ], _, _).
pairsAggregate([F1|T1],B,[F2|T2],R,[F|T]) :-
X1 =.. [B,F1,F2],
X2 =.. [R,X1,F],
X2,

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pairsAggregate(T1,B,T2,R,T).
subListAggregate(B, [ ], _, __).
subListAggregate(B, [F1|T1], R, [F2|T2]) :-
aggregate(B, F1, R, F2),
subListAggregate(B, T1, R, T2).
aggregate(B,[F|T],R,V) :-
aggrConstraint(B,T,F,S),
S1 =.. [R, S, V],
S1.
aggrConstraint(_, [ ], S, S).
aggrConstraint(B,[F|T],S1,S2) :-
S3 =.. [B, S1, F],
aggrConstraint(B, T, S3, S2).
... ...
The CSeE system allows users with CLP programming experience to access the CSeE library for
pre-defining other useful built-in predicates and adding constraint handling rules [8, 14] for
spreadsheet-related constraints. The module/2 directive declares the module name, and the public
(i.e., external visible) predicates of the module.
5.4. Program Integration
The CSeE system searches automatically two required constraints: ssVarRange(CR1) and
ssConstraintRange(CR2), for constructing a complete specification for a constraint satisfaction
problem. With the set of all cell variables, specified by the cell range CR1, and the set of
constraints, specified by CR2, the CSeE system can easily integrate all the finite-domain cell
variables and the defined constraints into an executable CLP program.
The structure of the integrated program is in a form as follows:
:- use_module(library(clpfd)).
:- use_module(library(csee)).
main(L) :- <S1>,
labeling([<S2>], L).
where <S1> is a set of generated constraints, <S2> is the optimization indicator, and L is a list of
cell variables corresponding to the cell range CR1. For each value in both cell ranges CR1 and
CR2, the system applies a corresponding constraint transformation rule described in Section 5.2
on the cell value and appends the converted result into the set <S1>. The optimization indicator
<S2> is determined by the user constraint ssMin(V) or ssMax(V) if any. Otherwise, <S2> will be
empty. The finite-domain solver module and the CSeE module are loaded at the beginning of the
program.

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6. EXAMPLES
We demonstrate how the CSeE system allows the EXCEL user to extend the spreadsheet’s ability
to solve certain constraint satisfaction problems. The examples include Sudoku, 8-Queen, and a
resource allocation problem.
6.1. The 8-Queen Problem
The 8-Queen problem deals with finding all the ways of placing eight queens on a square 8 × 8
chess board, in such a way that no two queens are on the same row, column, diagonal, or back-
diagonal. We use the cell matrix from A1 to H8 to represent the 8 × 8 chess board and numbers 1
and 0 in the cell to represent a queen and no queen, respectively. Figure 3 shows the Excel
interface for the 8-Queen problem.
Figure 3: An Excel interface for specifying the 8-Queen problem
The constraints ssVarRange(A1:H8) and ssConstraintRange(J4:J8) are required by the CSeE
system to specify the ranges of cell variables and other constraints, respectively. As shown in the
figure, we highlighted the cell matrix from A1 to H8 as they are cell variables. The constraint
ssDomain(A1:H8, 0, 1) is used to set the domain of the cell variables to a binary domain.
ssRowsAggregate(+,A1:H8,#=,1) means that there is one and at most one queen for each row;
similar is ssColsAggregate(+,A1:H8,#=,1) for each column. The relational operators #= and #=<
are equivalent to = and =<, respectively. The constraint ssDiagonalAggregate(+,A1:H8,#=<,1)
means that there is at most one queen for each diagonal; similarly,
ssBakDiagonalAggregate(+,A1:H8,#=<,1) is for each back diagonal.
Figure 4: An EXCEL interface after solving the 8-Queen problem
Figure 4 shows an interface where the constraints have been solved. As shown in the figure, the
CSeE system provides an add-in menu, named “CSeE system”, and a set of sub-menus, which are
responsible for communication between the spreadsheet layer and the constraint solving layer:

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• The “Parse/Build” menu item performs the following events sequentially: (i) check if the
ranges of cell variables and constraints have been specified; (ii) check if the constraints are
specified in a correct syntax; (iii) integrate all the inputs into an executable CLP program; (iv) run
the program on the SWI-Prolog; and (v) update the spreadsheet properly once answers to the
constraint satisfaction problem are generated and returned from the constraint solver.
• The “Solution Options” sub-menu provides the Excel user with the capability to traverse
different solutions, where the total number of solutions is indicated as well (e.g., 92 solution for
the 8-Queen problem).
• The item “Original State” restores the spreadsheet status before calling the constraint
solver so that the user can revise the constraints for new answers.
• The item “Add to CHRExcel lib” provides experienced CLP users with an interface to
update the CSeE library (e.g., adding new built-in predicates or constraint handling rules) for the
SWI-Prolog.
• The item “CSeE Help” shows the manual document which introduces the features of the
CSeE system and the instructions on how to use the spreadsheet-specific constraint language.
Scalability is an issue we have considered during the design of the SSCL constraint language. For
the 8-Queen program, the constraint specification can actually be easily scaled to larger problem
(e.g., 20-Queen) with very little change as follows:
ssDomain(A1:T20, 0, 1)
ssRowsAggregate(+, A1:T20, #=, 1)
ssColsAggregate(+, A1:T20, #=, 1)
ssDiagonalAggregate(+, A1:T20, #=<, 1)
ssBackDiagonalAggregate(+, A1:T20, #=<, 1)
6.2. The Sudoku Puzzle
The Sudoku puzzle is played in a 9 × 9 grid made up of 3 × 3 sub grids, where each grid contains
one instance of digits from 1 to 9. The numbers are entered in such a way that each row, column
and region (a 3 × 3 sub grid) has exactly one instance of the digit ranging from 1 to 9. There are
digits that are already fixed for some cells, and they act as clues that restrict the solution space in
such a way that there is one and only one correct way to populate the remaining regions.
Figure 5: An EXCEL interface for Sudoku

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We use the cell matrix from A1 to I9 to represent the 9 × 9 grid board. Figure 5 shows the
EXCEL interface for the Sudoku problem. The solution will be immediately shown once the
“Parse/Build” is clicked.
6.3. Job Assignment
The third example, as shown in Figure 6, is a simple machine/job assignment problem to
minimize the total cost. In Figure 6, the machine/job costs are shown in Table 1, and the
selection decision will be made in Table 2, including the minimal cost in the cell C16.
The minimization is specified by using the constraint ssMin(C16). Once the
“Parse/Build” is selected, the CSeE system will return a job assignment solution where
the cell variable C16 is minimized. For the example in Figure 6, the minimal cost in the
cell C16 will be 22. The order of constraints does not affect the solutions. Users can
specify those constraints in any cells except those variable cells.
Figure 6: An Excel interface for a job assignment example
7. CONCLUSIONS
Spreadsheet users encounter constraint satisfaction problems on a daily basis. As such, the CSeE
system is a framework incorporating the spreadsheet paradigm with a constraint solver so that it
becomes a useful and indispensable tool for the users. The CSeE’s seamless integration with
Excel and SWI-Prolog, along with its spreadsheet-specific language, extends the usefulness and
usability of the spreadsheet applications, especially for constraint solving problems.
The SSCL spreadsheet-specific constraint language allows users to specify constraints that exist
among the cells in a declarative and scalable way in order to solve constraint satisfaction
problems. Envisioned as a system that has potential for commercial value, the framework
significantly simplifies the complexity involved in developing solutions to many constraint-based
application domains from within the Excel’s spreadsheet interface. In addition, the CSeE system
provides an interface for CLP-experienced users to enhance constraint solving strategies.

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Authors
Ezana N. Beyenne received his B.S. and M.S. from the Computer Science Department of the University of
Nebraska at Omaha in 2006 and 2008, respectively. He has been working in the industry as a software
engineer since 2008. Currently he is a Senior Software Engineer working at Farm Credit Services of
America.
Dr. Hai-Feng Guo received his Ph.D. in Computer Science from New Mexico State University in 2001.
He is currently as Associate Professor in the Computer Science Department of the University of Nebraska
at Omaha. His research interests focus on declarative programming, constraint logic programming, and
software testing.